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What is cost complexity pruning Alpha?
Minimal cost complexity pruning recursively finds the node with the “weakest link”. The weakest link is characterized by an effective alpha, where the nodes with the smallest effective alpha are pruned first. As alpha increases, more of the tree is pruned, which increases the total impurity of its leaves.
What is Alpha in decision tree?
Alpha–beta pruning is a search algorithm that seeks to decrease the number of nodes that are evaluated by the minimax algorithm in its search tree. When applied to a standard minimax tree, it returns the same move as minimax would, but prunes away branches that cannot possibly influence the final decision.
What is the difference between Overfitting noisy data and pruning?
In machine learning and data mining, pruning is a technique associated with decision trees. Overfitting happens when a model memorizes its training data so well that it is learning noise on top of the signal. Underfitting is the opposite: the model is too simple to find the patterns in the data.
How to prune a tree with minimal cost complexity?
See also Minimal Cost-Complexity Pruning for details on pruning. Minimal cost complexity pruning recursively finds the node with the “weakest link”. The weakest link is characterized by an effective alpha, where the nodes with the smallest effective alpha are pruned first.
How to reduce the complexity of a decision tree?
In DecisionTreeClassifier, this pruning technique is parameterized by the cost complexity parameter, ccp_alpha. Greater values of ccp_alpha increase the number of nodes pruned.
How to choose the optimal value of Alpha?
As you mentioned, you can select an optimal value of alpha by using K-fold cross validation – build as large a tree as you can on each fold while aiming to minimize the cost complexity criterion for a different value of alpha. Averaging the results of all the trees and predicting on the kth fold would give you error rates for each alpha.
How is the biggest tree chosen for pruning?
If α = 0 then the biggest tree will be chosen because the complexity penalty term is essentially dropped. As α approaches infinity, the tree of size 1, i.e., a single root node, will be selected.